Abstract
Health intervention outcomes are often assessed as binomially distributed variables. In designing such interventions it is important to model the pre-intervention rate of the target behavior when performing sample size calculations. Unfortunately, the majority of sample size programs model post-intervention outcomes only, which results in exaggerated sample size estimates. An exception is Yoo and Spoth's (1993) conditional binomial method of sample size determination. This approach explicitly models pre-intervention behavior by focusing on baserate-adjusted post-intervention outcomes, and always results in smaller sample size estimates than conventional approaches. Advantages of the conditional binomial method are discussed and user-friendly software is presented.
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